{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day15 进阶构图元素——双坐标轴\n",
    "\n",
    "　　有些时候下我们希望在同一张图中绘制x相同，但各自对应的y不同的图层，就像下面的例子一样：\n",
    "  \n",
    "<center>\n",
    "    <img src=\"imgs/图1.png\" width=700>\n",
    "</center>\n",
    "\n",
    "　　这种y量纲不同但共享x轴的图层混合在同一张图中的情况，就是我们今天所要学习的知识点。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-01T02:27:51.585852Z",
     "iopub.status.busy": "2020-11-01T02:27:51.585852Z",
     "iopub.status.idle": "2020-11-01T02:27:51.885051Z",
     "shell.execute_reply": "2020-11-01T02:27:51.885051Z",
     "shell.execute_reply.started": "2020-11-01T02:27:51.585852Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matplotlib版本： 3.3.2\n"
     ]
    }
   ],
   "source": [
    "import matplotlib;print('matplotlib版本：', matplotlib.__version__)\n",
    "import matplotlib.pyplot as plt # 从matplotlib中导入我们最经常使用的pyplot子模\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] # 设定默认字体为SimHei以解决中文显示乱码问题\n",
    "plt.rcParams['axes.unicode_minus'] = False # 解决图像负号'-'不正常显示的问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　在`matplotlib`中，我们可以通过`twinx()`或`twiny()`来实现新增次坐标轴的目的，以`twinx()`为例，它的作用是创建一个新的`Axes`对象，且这个新的`Axes`对象与先前的`Axes`对象共享x轴的同时，又拥有着独立的y轴："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-01T02:27:51.887046Z",
     "iopub.status.busy": "2020-11-01T02:27:51.886049Z",
     "iopub.status.idle": "2020-11-01T02:27:52.059585Z",
     "shell.execute_reply": "2020-11-01T02:27:52.059585Z",
     "shell.execute_reply.started": "2020-11-01T02:27:51.887046Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib.lines import Line2D\n",
    "\n",
    "x = np.array(range(5))\n",
    "np.random.seed(0)\n",
    "y1 = np.random.uniform(size=5)\n",
    "y2 = np.random.uniform(0, 1000, 5)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 5))\n",
    "\n",
    "ax.bar(x, y1, facecolor='#cbe2e4', linestyle='--',\n",
    "       edgecolor='black', label='测试1')\n",
    "\n",
    "# 添加共享x轴的新Axes对象\n",
    "ax1 = ax.twinx()\n",
    "\n",
    "# 在ax1中叠加折线图层\n",
    "ax1.plot(x, y2, linestyle='--', marker='^', \n",
    "         color='#cc0033', linewidth=0.8,\n",
    "         markersize=8)\n",
    "\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(['1月', '2月', '3月', '4月', '5月']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　发现了吗，这个过程非常的简单自然，我们只需要利用`新Axes = 旧Axes对象.twinx()`的形式，就可以创建出默认y轴位于右侧的新`Axes`，且数量不限，每个也都可以用之前学习到的所有属性控制方法来调整，就像下面的例子一样："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-01T02:27:52.061581Z",
     "iopub.status.busy": "2020-11-01T02:27:52.060582Z",
     "iopub.status.idle": "2020-11-01T02:27:52.302972Z",
     "shell.execute_reply": "2020-11-01T02:27:52.302972Z",
     "shell.execute_reply.started": "2020-11-01T02:27:52.061581Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 5))\n",
    "\n",
    "ax.bar(x, y1, facecolor='#cbe2e4', linestyle='--',\n",
    "       edgecolor='black', label='测试1')\n",
    "\n",
    "# 添加共享x轴的新Axes对象\n",
    "ax1 = ax.twinx()\n",
    "ax2 = ax.twinx()\n",
    "ax3 = ax.twinx()\n",
    "\n",
    "# 把第2个twinx坐标轴对应的y轴右移30个单位\n",
    "ax2.spines['right'].set_position(('outward', 30))\n",
    "ax2.spines['right'].set_color('red')\n",
    "\n",
    "# 把第3个twinx坐标轴对应的y轴移到左边\n",
    "ax3.yaxis.tick_left()\n",
    "ax3.spines['left'].set_position(('outward', 30))\n",
    "ax3.spines['left'].set_color('blue')\n",
    "\n",
    "# 在ax1中叠加折线图层\n",
    "ax1.plot(x, y2, linestyle='--', marker='^', \n",
    "         color='#cc0033', linewidth=0.8,\n",
    "         markersize=8)\n",
    "\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(['1月', '2月', '3月', '4月', '5月']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　如果要将不同`Axes`对象中的图层元素组合成一个图例，那么就可以灵活使用到之前学到过的技巧："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-01T02:27:52.303931Z",
     "iopub.status.busy": "2020-11-01T02:27:52.303931Z",
     "iopub.status.idle": "2020-11-01T02:27:52.458553Z",
     "shell.execute_reply": "2020-11-01T02:27:52.458553Z",
     "shell.execute_reply.started": "2020-11-01T02:27:52.303931Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.lines import Line2D\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 5))\n",
    "\n",
    "ax.bar(x, y1, facecolor='#cbe2e4', linestyle='--',\n",
    "       edgecolor='black', label='测试1')\n",
    "\n",
    "# 添加共享x轴的新Axes对象\n",
    "ax1 = ax.twinx()\n",
    "\n",
    "# 在ax1中叠加折线图层\n",
    "ax1.plot(x, y2, linestyle='--', marker='^', \n",
    "         color='#cc0033', linewidth=0.8,\n",
    "         markersize=8)\n",
    "\n",
    "handles = [\n",
    "    Line2D([], [], linestyle='--', \n",
    "           marker='^', color='#cc0033',\n",
    "           linewidth=0.8, markersize=8)\n",
    "]\n",
    "\n",
    "og_handles, labels = ax.get_legend_handles_labels()\n",
    "\n",
    "ax.legend(handles=og_handles+handles, labels=labels+['测试2'])\n",
    "\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(['1月', '2月', '3月', '4月', '5月']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 课后测验：\n",
    "\n",
    "　　接下来的时间交给你们，还记得开头的示例图片吗，我用`matplotlib`仿了一个版本如下图所示：\n",
    "\n",
    "<center>\n",
    "    <img src='imgs/模仿.png' width=650>\n",
    "    </img>\n",
    "</center>\n",
    "  \n",
    "　　请在下面代码的基础上，补充缺失的部分，尽可能还原上图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-11-01T02:27:52.459514Z",
     "iopub.status.busy": "2020-11-01T02:27:52.459514Z",
     "iopub.status.idle": "2020-11-01T02:27:52.463539Z",
     "shell.execute_reply": "2020-11-01T02:27:52.463539Z",
     "shell.execute_reply.started": "2020-11-01T02:27:52.459514Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置字体为Times New Roman，关于字体的内容之后会单独介绍\n",
    "plt.rcParams[\"font.family\"] = \"Times New Roman\"\n",
    "\n",
    "x = np.array(range(5))\n",
    "A = np.array([100, 110, 125, 90, 125])\n",
    "B = np.array([103, 140, 75, 75, 100])\n",
    "C = np.array([140, 150, 160, 165, 140])\n",
    "D = np.array([70, 155, 145, 160, 20])\n",
    "y = A+B+C+D\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "\n",
    "ax.bar(x, A, color='#7bc0f7', width=0.16, label='Product A')\n",
    "\n",
    "ax.bar(x+0.2, B, color='#3b8ad9', width=0.16, label='Product B')\n",
    "\n",
    "ax.bar(x+0.4, C, color='#f18226', width=0.16, label='Product C')\n",
    "\n",
    "ax.bar(x+0.6, D, color='#ffdb69', width=0.16, label='Product D')\n",
    "\n",
    "ax.set_ylim(0, 200)\n",
    "ax.set_yticks([0, 50, 100, 150, 200])\n",
    "\n",
    "ax.set_xticks(x+0.3)\n",
    "ax.set_xticklabels(['Strategy 1', 'Strategy 2', 'Strategy 3', 'Strategy 4', 'Strategy 5'])\n",
    "\n",
    "ax.spines['top'].set_color('none')\n",
    "ax.spines['left'].set_color('lightgrey')\n",
    "ax.spines['bottom'].set_color('lightgrey')\n",
    "ax.spines['right'].set_color('none')\n",
    "\n",
    "ax.tick_params(axis='y', color='#595959', labelcolor='#595959',\n",
    "               labelsize=12)\n",
    "ax.tick_params(axis='x', color='none', labelcolor='#595959',\n",
    "               labelsize=12)\n",
    "\n",
    "ax.set_ylabel(\"Product's values\", color='#262626', fontsize=14)\n",
    "\n",
    "ax.set_title('Compare sales strategy', color='#262626', fontsize=20, pad=30)\n",
    "\n",
    "'''补充你的代码'''\n",
    "#添加新的ax对象\n",
    "ax1 = ax.twinx()\n",
    "ax1.plot(x+0.3,A+B+C+D,color='black',linewidth=2)\n",
    "ax1.set_ylim(0,600)\n",
    "ax1.set_yticks([0,200,400,600])\n",
    "ax1.spines['top'].set_color('none')\n",
    "ax1.spines['left'].set_color('lightgrey')\n",
    "ax1.spines['bottom'].set_color('lightgrey')\n",
    "ax1.spines['right'].set_color('lightgrey')\n",
    "ax1.tick_params(axis='y', color='#595959', labelcolor='#595959',\n",
    "               labelsize=12)\n",
    "ax1.set_ylabel(\"Total of amount\", color='#262626', labelpad=20,fontdict={\n",
    "                       'fontsize': 14, # 控制字体大小\n",
    "                       'rotation': -90 # 控制文字旋转角度\n",
    "                   })\n",
    "#增加图例\n",
    "handles=[\n",
    "    Line2D([],[],\n",
    "          color='black',\n",
    "          linewidth=2)\n",
    "]\n",
    "og_handles,labels = ax.get_legend_handles_labels()\n",
    "ax.legend(handles=og_handles+handles,labels=labels+['Total'],ncol=5,loc='upper center',frameon=False,\n",
    "          borderpad=-2)\n",
    "\n",
    "fig.set_facecolor('white')\n",
    "\n",
    "fig.savefig('模仿.png', dpi=300, pad_inches=0.1, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 请在今天对应的帖子下回复补充过注释的代码截图"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
